Theory refinement combining analytical and empirical methods

Ourston, Dirk; Mooney, Raymond J.

doi:10.1016/0004-3702(94)90028-0

Cited by 118 publications

(63 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most research in this area has been concerned with combining empirical (i.e., purely inductive) approaches with analytical ones (e.g., Ourston & Mooney, 1994;Towell & Shavlik, 1994;Pazzani & Kibler, 1992; see also Michalski & Tecuci, 1993). The expression "empirical multi-strategy learning" will therefore be used to distinguish the case where all the components are empirical.…”

Section: The Empirical Multi-strategy Learning Problemmentioning

confidence: 99%

Unifying instance-based and rule-based induction

1996

View full text Add to dashboard Cite

Abstract. Several well-developed approaches to inductive learning now exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a unification of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally specific rules, and classification is performed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be efficient. It is implemented in the RISE 3.1 system. In an extensive empirical study, RISE consistently achieves higher accuracies than state-ofthe-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Lesion studies show that each of RISE's components is essential to this performance. Most significantly, in 14 of the 30 domains studied, RISE is more accurate than the best of PEBLS and CN2, showing that a significant synergy can be obtained by combining multiple empirical methods.

show abstract

Section: The Empirical Multi-strategy Learning Problemmentioning

confidence: 99%

Unifying instance-based and rule-based induction

1996

View full text Add to dashboard Cite

show abstract

“…There has been a growing literature on automated "theory refinement" (Fu, 1989;Ginsberg, 1988;Ourston & Mooney, 1994;Pazzani & Kibler, 1992;Shavlik & Towell, 1989), and it is from this research perspective that our advice-taking work arose. Our new work differs by its novel emphasis on theory refinement in the context of multi-step problem solving in multi-actor worlds, as opposed to refinement of theories for categorization and diagnosis.…”

Section: Refining Prior Knowledgementioning

confidence: 99%

Creating advice-taking reinforcement learners

Maclin

Shavlik

1996

Mach Learn

View full text Add to dashboard Cite

Abstract. Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present and evaluate a design that addresses this shortcoming by allowing a connectionist Q-learner to accept advice given, at any time and in a natural manner, by an external observer. In our approach, the advice-giver watches the learner and occasionally makes suggestions, expressed as instructions in a simple imperative programming language_ Based on techniques from knowledge-based neural networks, we insert these programs directly into the agent's utility function. Subsequent reinforcement learning further integrates and refines the advice. We present empirical evidence that investigates several aspects of our approach and shows that, given good advice, a learner can achieve statistically significant gains in expected reward. A second experiment shows that advice improves the expected reward regardless of the stage of training at which it is given, while another study demonstrates that subsequent advice can result in further gains in reward. Finally, we present experimental results that indicate our method is more powerful than a naive technique for making use of advice.

show abstract

“…There are many systems for theory revision in propositional and predicate logic (e.g., Koppel, Feldman, & Segre, 1994;Ourston & Mooney, 1994;Richards & Mooney, 1995;Towell & Shavlik, 1993). For instance, the EITHER system (Ourston & Mooney, 1994) uses deductive, abductive, and inductive components to revise a propositional Horn theory from a given set of counterexamples, and the KBANN system (Towell & Shavlik, 1993 uses neural networks for the same task.…”

Section: Related Workmentioning

confidence: 99%

“…1 This is the problem known in machine learning as theory revision (or knowledge-base revision) (e.g., Towell & Shavlik, 1993;Ourston & Mooney, 1994;Richards & Mooney, 1995;Koppel, Feldman, & Segre, 1994). Note that the artificial intelligence term theory, as used in this paper, has the same meaning as what the computational learning theory community calls a concept.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2002

View full text Add to dashboard Cite

Abstract. The theory revision, or concept revision, problem is to correct a given, roughly correct concept. This problem is considered here in the model of learning with equivalence and membership queries. A revision algorithm is considered efficient if the number of queries it makes is polynomial in the revision distance between the initial theory and the target theory, and polylogarithmic in the number of variables and the size of the initial theory. The revision distance is the minimal number of syntactic revision operations, such as the deletion or addition of literals, needed to obtain the target theory from the initial theory. Efficient revision algorithms are given for three classes of disjunctive normal form expressions: monotone k-DNF, monotone m-term DNF and unate two-term DNF. A negative result shows that some monotone DNF formulas are hard to revise.

show abstract

Theory refinement combining analytical and empirical methods

Cited by 118 publications

References 11 publications

Unifying instance-based and rule-based induction

Unifying instance-based and rule-based induction

Creating advice-taking reinforcement learners

Untitled

Contact Info

Product

Resources

About